A support vector machine approach to a classification problem in robotics

Wang, Jianxiong. (2004). A support vector machine approach to a classification problem in roboticsMPhil Thesis, School of Information Technology and Electrical Engineering, The University of Queensland.

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The problem of simultaneous localization and mapping (SLAM) has been the subject of widespread study in the field of robotics in recent years. Researchers into SLAM are aiming to incrementally build a map of the environment while concurrently using this map to compute robot location. An efficient solution to the problem depends on accurate measurement of the environment which in turn requires a robust and quick method to amalgamate data from various sensors.

This thesis is concerned with one component of the SLAM problem, namely the problem of object recognition. For this problem the robot we employ has an array of sonar sensors and these provide the data we employ for the object recognition task. The sonar data is preprocessed and then fed to a support vector machine (SVM) classifier to carry out the recognition.

The thesis describes the use of genetic algorithms (GAs) to assist the SVM recognition process in two novel ways. It is shown how a GA can be employed firstly to optimize the tuning parameters of an SVM classifier and secondly to select a sequence of subsets of the training data so that the generalization performance of the SVM gradually improves.